needs a concerted international effort without boundaries, supporting collaborative and integrative research between experts from seven fields: ❶ data science, ❷ algorithms, ❸ network science, ❹ graphs/topology, ❺ time/entropy, ❻ visualization, and ❼ privacy, data protection, safety and security.

Artificial Intelligence (AI) powered by Machine learning (ML) is the most growing field in computer science today and the health domain is among the greatest application challenges. The grand vision is in automatic ML algorithms. Recent advances e.g. in speech recognition, recommender systems, or autonomous vehicles demonstrate impressive progress. Automatic approaches greatly benefit from big data with many training sets. However, the application of automatic machine learning (aML) in the health domain, where we are confronted with complex, heterogeneous, high-dimensional, probabilistic, uncertain, incomplete, noisy, imbalanced data and sometimes with small amounts of data, or rare events, seems elusive in the near future. A good example are Gaussian processes, where aML (e.g. standard kernel machines) struggle on function extrapolation problems which are trivial for human learners. The perfect example are Deep Learning (DL) approaches which have great potential in many domains,  including the health domain, but the limitations of such approaches are exactly in their autonmous behaviour. Due to raising legal issues (e.g. due to the EU GDPR) such automatic approaches become difficult to use in the future. Consequently, the field of explainable AI [1] becomes more and more important. Explainable AI develops methods for making such deep learning approaches transparent. However, for diagnostic and educational purposes there is a need to go beyond explainable AI; For example, to reach a level of explainable medicine there is a crucial need for causability. Causability [2] is different from Causality [3] but closely connected: Causability provides measurements for ensuring the quality of explanations produced by explainable AI methods and to enable the (human) experts to understand why an algorithm came up with a certain result, or why a result had a certain error rate. This calls for contextual understanding which can be fostered by bringing a human-in-the-loop [4], which adds the component of human expertise to AI processes.

A synergistic combination of methodologies and approaches of two areas offer ideal conditions towards unraveling these challenges and to foster new, efficient and user-friendly algorithms and human-AI interfaces: Human-Computer Interaction (HCI) and Knowledge Discovery/Data Mining (KDD), with the goal of supporting human intelligence with computational intelligence.

integrative interactive machine learning human-in-the-loop

The knowledge discovery pipeline needs a concerted cross-disciplinary effort of diverse experts

The cross-domain integration and appraisal of different fields shall provide an atmosphere to foster different perspectives and opinions; it will offer a platform for novel ideas and a fresh look on the methodologies to support crazy ideas – and at the end of the day to put these ideas into Business. The initial idea can be read [here],<springerlink>.

The mission of the international expert network HCI-KDD is to bring together diverse researchers sharing a common vision and to stimulate crazy ideas and a fresh look to methodologies from other disciplines without any boundaries, encouraging multi-disciplinary work, to bundle synergies, to participate in joint project proposals for getting funding on various levels, inclusive travel funds, international student exchange and promoting young and early-stage researchers.

The expert network organizes special sessions at least once a year, see e.g. [1st – Graz], [2nd – Macau], [3rd – Maribor], [4th – Regensburg], [5th Lisbon], [6th Warszawa], [7th Banff], [8th London], [9th Salzburg], [10a Reggio di Calabria][10b Reggio di Calabria], and ultimately resulted in the CD-MAKE: International Cross-Domain Conference for Machine Learning and Knowledge Extraction

CD stands for Cross-Domain !

Some recent example outputs of our concerted effort can be seen here:

Springer Lecture Notes in Computer Science LNCS 11713

Springer Lecture Notes in Computer Science LNCS 11015

Springer Lecture Notes in Computer Science LNCS 10410

Springer Lecture Notes in Artificial Intelligence LNAI 9605

Springer Lecture Notes in Computer Science LNCS 8700

Springer Lecture Notes in Computer Science LNCS 8401

Springer Lecture Notes in Computer Science LNCS 7947

Springer Lecture Notes in Computer Science LNCS 7058

Springer Lecture Notes in Computer Science LNCS 6389

concerted effort of the HCI-KDD international expert network

Integrative Machine Learning needs a concerted effort

International Scientific Committee:

MED = Medical Doctor (“doctor-in-the-loop”); IND = Industry Member; ESR = Early Stage Researcher, e.g. PhD-Student)